{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.9"
    },
    "colab": {
      "name": "density-pi-plot.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPvBMrKBNA2e",
        "colab_type": "code",
        "outputId": "cb5259a4-27a6-4371-fd68-62ef4646c3c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        }
      },
      "source": [
        "!pip install GPy\n",
        "!sudo apt-get install msttcorefonts -qq"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: GPy in /usr/local/lib/python3.6/dist-packages (1.9.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from GPy) (1.12.0)\n",
            "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.6/dist-packages (from GPy) (1.4.1)\n",
            "Requirement already satisfied: paramz>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from GPy) (0.9.5)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from GPy) (1.18.3)\n",
            "Requirement already satisfied: decorator>=4.0.10 in /usr/local/lib/python3.6/dist-packages (from paramz>=0.9.0->GPy) (4.4.2)\n",
            "E: Failed to fetch http://archive.ubuntu.com/ubuntu/pool/main/u/update-manager/python3-update-manager_18.04.11.10_all.deb  404  Not Found [IP: 91.189.88.142 80]\n",
            "E: Failed to fetch http://archive.ubuntu.com/ubuntu/pool/main/u/update-manager/update-manager-core_18.04.11.10_all.deb  404  Not Found [IP: 91.189.88.142 80]\n",
            "E: Unable to fetch some archives, maybe run apt-get update or try with --fix-missing?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRwE4DSNMjZH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import json\n",
        "import warnings\n",
        "import itertools\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# from sklearn.gaussian_process.kernels import Matern\n",
        "from GPy.mappings.constant import Constant\n",
        "from GPy.kern import Matern52\n",
        "from GPy.models import GPRegression\n",
        "import GPy\n",
        "\n",
        "from scipy.special import ndtr\n",
        "from scipy.stats import norm\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "# plt.style.use('classic')\n",
        "plt.style.use('seaborn-paper')\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j_VAAQcMjZW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plotting styles\n",
        "\n",
        "def nnsvm(rcParams, i=0):\n",
        "    rcParams['font.family'] = 'serif'\n",
        "    rcParams['font.serif'] = 'Ubuntu'\n",
        "    rcParams['font.monospace'] = 'Ubuntu Mono'\n",
        "    rcParams['font.size'] = 12 + i\n",
        "    rcParams['axes.labelsize'] = 12 + i\n",
        "    rcParams['axes.labelweight'] = 'normal'\n",
        "    rcParams['xtick.labelsize'] = 10 + i\n",
        "    rcParams['ytick.labelsize'] = 10 + i\n",
        "    rcParams['legend.fontsize'] = 12 + i\n",
        "    rcParams['figure.titlesize'] = 14 + i\n",
        "    rcParams['lines.linewidth']= 2.7\n",
        "    rcParams['axes.titlesize'] = 14 + i\n",
        "alpha_plt = 0.4 # beautification\n",
        "    \n",
        "# gifs stuff\n",
        "gifDir = 'MAB_gifs'\n",
        "os.makedirs(gifDir, exist_ok=True)\n",
        "delay = 80"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3suPJAKMjZg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SPINE_COLOR = 'gray'\n",
        "\n",
        "def format_axes(ax):\n",
        "    \n",
        "    for spine in ['top', 'right']:\n",
        "        ax.spines[spine].set_visible(False)\n",
        "\n",
        "    for spine in ['left', 'bottom']:\n",
        "        ax.spines[spine].set_color(SPINE_COLOR)\n",
        "        ax.spines[spine].set_linewidth(0.5)\n",
        "\n",
        "    ax.xaxis.set_ticks_position('bottom')\n",
        "    ax.yaxis.set_ticks_position('left')\n",
        "\n",
        "    for axis in [ax.xaxis, ax.yaxis]:\n",
        "        axis.set_tick_params(direction='out', color=SPINE_COLOR)\n",
        "        \n",
        "    # ax.grid(alpha=0.2)\n",
        "\n",
        "    return ax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMHJIRoxMjZv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gp_creator(x, y, val = 5):\n",
        "    y.resize(x.shape[0], 1)\n",
        "    ker = Matern52(x.shape[1], ARD=True, variance=1)\n",
        "#     print (x, x.mean())\n",
        "    mean_fn = Constant(x.shape[1], 1, value=val)\n",
        "    gp = GPRegression(x, y, kernel=ker, noise_var=0,\n",
        "                      mean_function = mean_fn)\n",
        "    gp.optimize()\n",
        "    return gp"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rWfGmQ6MjZ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f(x):\n",
        "    \"\"\"The function to predict.\"\"\"\n",
        "    return 2*((x-3)*np.sin((x-3))) + 2 + 0.5*x\n",
        "\n",
        "nnsvm(plt.rcParams, -2)\n",
        "x = np.atleast_2d(np.linspace(0, 6, 25000)).T\n",
        "y_pred, sigma = 5*np.ones(x.shape).flatten(), 1.1*np.ones(x.shape).flatten()\n",
        "\n",
        "train_X = np.array([[.3],[2.2], [3.7]])\n",
        "train_y = f(train_X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZjBiUUQMjaB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_gray(n, ax):\n",
        "    y1 = mu-n*std\n",
        "    y2 = mu+n*std\n",
        "    y_temp = y_max_eps*np.ones_like(y1)\n",
        "    ax.fill_between(\n",
        "        x,y2,y1,where=y1>=y_temp,\n",
        "        color='violet',alpha=alphas[n],\n",
        "        interpolate=False, label=labels[n], linewidth=0\n",
        "    )\n",
        "    ax.fill_between(\n",
        "        x,y2,y_temp,where=y2>=y_temp,\n",
        "        color='violet',alpha=alphas[n],\n",
        "        interpolate=False, linewidth=0\n",
        "    )\n",
        "    ax.fill_between(\n",
        "        x,y2,y1,where=y2<=y_temp,\n",
        "        color='grey',alpha=alphas[n],\n",
        "        interpolate=False, linewidth=0\n",
        "    )\n",
        "    ax.fill_between(\n",
        "        x,y1,y_temp, where=y2>=y_temp,\n",
        "        color='grey',alpha=alphas[n],\n",
        "        interpolate=False, linewidth=0\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3Qu3VSzMjaG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nnsvm(plt.rcParams, 3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1rYmrHPMjaL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ACQ:\n",
        "    def acq_fn(self, *args, **kwargs):\n",
        "        raise NotImplemented\n",
        "    def __str__(self):\n",
        "        return self.__class__.__name__\n",
        "    def __call__(self, *args, **kwargs):\n",
        "        return self.acq_fn(*args, **kwargs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cH4sMtf_MjaR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code is heavily borrowed from: https://modal-python.readthedocs.io/\n",
        "# en/latest/_modules/modAL/acquisition.html#max_PI\n",
        "class PI(ACQ):\n",
        "    def acq_fn(\n",
        "        self, gp, x, mu=5., eps=0.01, **kwargs):\n",
        "        \"\"\"\n",
        "        gp: sklearn.GPRegresssor\n",
        "        \"\"\"\n",
        "        y_pred, var = [t.flatten() for t in gp.predict(x)]\n",
        "        sigma = np.sqrt(var).squeeze()\n",
        "        \n",
        "        cdf = ndtr((y_pred - mu - eps)/sigma)\n",
        "        return cdf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrifRSt6MjaX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gp = gp_creator(train_X, train_y)\n",
        "mu, var = gp.predict(x)\n",
        "mu = mu.squeeze()\n",
        "std = np.sqrt(var).squeeze()\n",
        "# x = x.flatten()\n",
        "# gp.plot(plot_density=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hm_wr0gYMjaa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = PI()\n",
        "acq = p.acq_fn(gp, x, train_y.max().item(), eps=0.075)\n",
        "x = x.flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FB3LxyYyQIof",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9TEM3tVMjae",
        "colab_type": "code",
        "outputId": "cd099069-8ab1-44ed-c271-c8ef3dd382c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "y_max_eps = mu.max()\n",
        "fig = plt.figure(figsize=(16, 10))\n",
        "# fig, ax = plt.subplots(nrows=2, figsize=(16, 6))\n",
        "\n",
        "ax = []\n",
        "ax.append(plt.subplot2grid((5, 1), (0, 0), rowspan=3))\n",
        "ax.append(plt.subplot2grid((5, 1), (3, 0), rowspan=2))\n",
        "\n",
        "# plt.figure(figsize=(14, 8))\n",
        "ix = np.argmax(acq)\n",
        "ax[1].plot(x, acq, color=\"fuchsia\")\n",
        "ax[1].scatter([x[ix]], [acq.max()], marker='*', zorder = 10,\n",
        "              c=\"navy\", s=150, label=r\"Largest $\\alpha_{PI}$\")\n",
        "ax[0].plot(x, mu, color='k', lw=3)\n",
        "\n",
        "alphas = {1:0.4, 2:0.2, 3:0.1, 4:0.05}\n",
        "labels = {x:r'$\\mu \\pm %s\\sigma$' %x for x in [1, 2, 3, 4]}\n",
        "\n",
        "ax[0].axhline(y_max_eps, color='orange', lw=2.5, label=r'$f(x^+) + \\epsilon$')\n",
        "ax[0].axvline(x[ix], linestyle=\"-.\", lw=2)\n",
        "ax[1].axvline(x[ix], linestyle=\"-.\", lw=2, label = \"Next Query Point\")\n",
        "\n",
        "\n",
        "plot_gray(1, ax[0])\n",
        "plot_gray(2, ax[0])\n",
        "plot_gray(3, ax[0])\n",
        "plot_gray(4, ax[0])\n",
        "\n",
        "ax[0].legend()\n",
        "ax[1].legend()\n",
        "ax[1].set_xlabel(\"X\")\n",
        "ax[0].set_ylabel(\"Gold Content\")\n",
        "ax[1].set_ylabel(r\"Acquisition function ($\\alpha_{PI}(x)$)\")\n",
        "ax[0].set_xlim((x.min(), x.max()+.5))\n",
        "ax[1].set_xlim((x.min(), x.max()+.5))\n",
        "ax[0].set_xticks([])\n",
        "format_axes(ax[0])\n",
        "format_axes(ax[1])\n",
        "plt.savefig(\"MAB_gifs/density_pi.png\", bbox_inches=\"tight\", dpi=180)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGUmprGfMjal",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAPq7Np7Mjao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vbhvg8quMjaq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}